Machine learning-based predictive modeling of depression in hypertensive populations.

ABSTRACT: We aimed to develop prediction models for depression among U.S. adults with hypertension using various machine learning (ML) approaches. Moreover, we analyzed the mechanisms of the developed models. This cross-sectional study included 8,628 adults with hypertension (11.3% with depression) from the National Health and Nutrition Examination Survey (2011-2020). We selected several significant features using feature selection methods to build the models. Data imbalance was managed with random down-sampling. Six different ML classification methods implemented in the R package caret-artificial neural network, random forest, AdaBoost, stochastic gradient boosting, XGBoost, and support vector machine-were employed with 10-fold cross-validation for predictions. Model performance was assessed by exa

SUBMITTER: Lee C 

PROVIDER: S-EPMC9337649 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature